Clinical Research: Phase 1 - Phase 4

Ten Tips for Establishing the Clinical Utility of Molecular Diagnostics: Stepping Stones to Precision Medicine


The strongest foundation for successful widespread marketing of any diagnostic is evidence of clinical utility. Clinical validation – determining a test’s ability to diagnose a health condition or predict risk of clinical outcomes – may fail to convince physicians and especially healthcare payers to accept a new diagnostic. Failure to gain payer acceptance may even result in business failure for the developer. This was the unfortunate result for Predictive Biosciences and its non-invasive, urine-based diagnostic for bladder cancer. Predictive’s CertNDx test for urothelial cancer utilized protein biomarkers and molecular DNA analysis to stratify patients based on the likelihood of cancer and disease progression and potentially offered substantial advantages, especially for patients, over cystoscopy, the standard invasive diagnostic procedure for bladder cancer.

BioCentury summed up the situation facing many molecular diagnostics companies: Empowered by a new CMS coding system that for the first time gives payers information about the molecular diagnostic tests they are being asked to cover, Medicare contractors are increasingly making coverage contingent on evidence of clinical utility – evidence that in many cases labs do not possess. The cost of demonstrating clinical utility, along with the lack of clear or consistent standards, is killing a business model that has made it possible for small companies to commercialize molecular diagnostics quickly and cheaply.

A 2013 technology assessment of three additional genetic or molecular pathology tests – CancerTypeID, miRview, and PathworkDx – also concluded that evidence of effect of the tests on treatment decisions and patient outcomes was insufficient.2 Similarly, a 2014 technology assessment of 11 molecular pathology tests for common cancers found no evidence to determine whether using such tests to estimate prognosis leads to improved patient outcomes.3 A decade later, diagnostics developers are facing the same questions.

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